Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors

Authors

  • David Shamma Yahoo! Research
  • Jude Yew University of Michigan
  • Lyndon Kennedy Yahoo! Research
  • Elizabeth Churchill Yahoo! Research

Abstract

In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video's view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools.

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Published

2021-08-03

How to Cite

Shamma, D., Yew, J., Kennedy, L., & Churchill, E. (2021). Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 618-621. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14154